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1CP2-P-10.5 AI personal data and ethical legal issues checkpoint

Part 5 of 5 · 1CP2-P-10 · AI, personal data and ethical/legal issues

This final principles checkpoint assesses AI/ML/robotics, accountability, safety, bias, liability, personal-data collection, privacy, misuse and data-protection principles. Accurate mechanism comes before judgement.

Students will:

  • explain AI and machine-learning mechanisms accurately
  • connect bias, safety and accountability to consequences
  • apply personal-data and privacy principles
  • plan a developed ethical and legal discussion

Inside: 3 explanation cells, 1 fill-in-the-blanks cell, 3 multiple-choice questions and 5 written answers. 20 marks, about 45 minutes.

Series: 1CP2-P-10 · AI, personal data and ethical/legal issues, part 5 of 5.

Shared by Coding PathwayVerified teacher

  • 12 cells
  • About 45 minutes
  • CC BY-SA 4.0
  • Shared 17 Aug 2026

Preview

The whole resource, exactly as a class sees it. Answers and marking are held back.

AI, personal data and ethical/legal issues checkpoint

This final principles checkpoint assesses AI/ML/robotics, accountability, safety, bias, liability, personal-data collection, privacy, misuse and data-protection principles. Accurate mechanism comes before judgement.

Fill in the blanks3 marks
Learning patterns from examples is gap 1. A systematic unfair tendency is algorithmic gap 2. Collecting only necessary data applies gap 3.
  • bias
  • machine learning
  • minimisation
Multiple choice1 mark

A warehouse arm follows fixed programmed coordinates and does not learn. Which is the best classification?

  • AA robot, but not necessarily ML
  • BAlways ML
  • COnly a database
  • DAlgorithmic bias
Written answer3 marks

An insurance model was trained on incomplete historical data and rejects more applicants from one postcode. Explain the algorithmic-bias chain.

Link data, decision and impact.

Students type their answer here.

Written answer2 marks

Recommend one control for the insurance model and explain how it reduces risk.

Use evidence, oversight or a challenge route.

Students type their answer here.

Multiple choice1 mark

Which is most likely personal data?

  • AAn account ID linked to one person's location history
  • BAn unrelated random value
  • CA fictional name in a made-up example
  • DA blank file
Written answer2 marks

A fitness app keeps precise location forever after it is no longer needed. Identify one relevant data-protection principle and apply it.

Name the principle and correction.

Students type their answer here.

Plan the extended response

A strong discussion identifies the system and stakeholders, explains how inputs lead to decisions, develops benefits and risks, proposes proportionate safeguards and reaches a justified conclusion. The response below should be continuous reasoning, not disconnected slogans.

Written answer6 marks

A school is considering an AI system that analyses attendance, assessment and behaviour records to predict which students may need support. Discuss the ethical and legal issues the school should consider.

Address useful intervention, accuracy/bias, privacy, purpose, security, human oversight, affected students and a justified conclusion.

Students type their answer here.

Multiple choice1 mark

Which statement best separates ethical and legal reasoning?

  • AEthical and legal always mean the same
  • BEthics asks what ought to be fair or acceptable; law/data duties set required handling, and the two can overlap
  • CAnything legal is automatically ethical
  • DOnly computers have liability
Written answer1 mark

State one condition that would make deployment more acceptable.

Give a specific safeguard.

Students type their answer here.

Route forward

The worksheet collection is complete. Continue with unseen Pearson-style questions, timed six-mark responses and full papers so knowledge can be selected without worksheet cues.